🚀 microvec is a lightweight micro-vector database in pure Python. If you're building RAG pipelines, semantic search, or anything with embeddings and don't want to spin up a full vector DB service, this is for you.  What it does:  - Store vectors alongside document text and metadata  - Search by cosine, euclidean, or dot product similarity  - Filter results with any Python predicate before scoring  - Batch insert, update, and delete nodes  - Persist to disk safely (no pickle — JSON + numpy format)  What's new in v0.1.0:  - Complete rewrite from prototype to production-ready library  - Search results now return the actual document text, not just an index  - Full input validation with descriptive errors  - 107 tests, 98.5% coverage, mypy strict, ruff clean  Install: pip install microvec  No servers. No config. Just embeddings.  GitHub: https://lnkd.in/dnxWycy9

Open-Source Python Suite Automates Multi-Platform Social Analytics and Notion Reporting 📌 Roberto Ferraro’s open-source Python suite automates cross-platform social analytics-pulling data from LinkedIn, Instagram, Twitter/X, Threads, and Substack-and syncs insights directly into Notion. No more manual spreadsheets or fragmented reports: this modular system runs daily, caches API calls, and transforms raw data into actionable metrics with PostgreSQL backing. Engineering teams can now turn content performance into real-time intelligence-automatically, effortlessly. 🔗 Read more: https://lnkd.in/dV9NwZb4 #Pythonsuite #Postgresql #Notionreporting #Socialanalytics #Multiplatform

🚀 Handling Data Validation in Django APIs – A Small Learning While working on backend APIs, I realized that handling invalid data properly is just as important as building the API itself. Here’s what I learnt: 🔹 Validating incoming request data before processing 🔹 Using serializers to enforce data structure and constraints 🔹 Returning meaningful error messages instead of generic responses 🔹 Preventing invalid data from entering the database 💡 One thing I found interesting: Even a small validation miss can lead to inconsistent data and unexpected bugs later. This made me understand how important it is to design APIs carefully, not just for functionality but also for reliability. Still exploring better ways to handle validations and edge cases 🚀 #Django #Python #BackendDevelopment #API #WebDevelopment #LearningInPublic

I was teaching a class on serverless backend. Lambda, DynamoDB, API Gateway. The code was clean. The logic was simple. Read from the database, update a value, return the result. We hit Test. "Object of type Decimal is not JSON serializable." Everything looked fine. The DynamoDB item was there. The number was a number. So why was Python refusing to serialize it? Here is what nobody tells you. DynamoDB does not return integers. It returns Decimal objects. Python's json.dumps cannot serialize Decimal. So it breaks. The fix is one word. int() 15 minutes debugging. 3 seconds to fix once you know. #AWS #DynamoDB #Lambda #Serverless #Python

🚀 Built a Lightweight File-Based Database in Python Over the past few days, I worked on a project called mini-DB — a simple yet powerful key-value database built from scratch using Python. 🔹 What it does: Stores data in JSON files 📁 Supports basic operations: create, get, set, update Uses file-level locking to handle concurrent access 🔒 Provides a clean CLI interface using argparse ⚡ 🔹 Why I built this: I wanted to understand how databases actually work under the hood — especially concepts like: Data persistence Concurrency control Storage abstraction Instead of just using tools like Redis or MongoDB, I tried building a minimal version myself. 🔹 Key learnings: Designing layered architecture (Storage → Connector → Interface) Handling race conditions with file locks Building CLI tools that feel like real-world systems Example usage: python tool.py master set user Jashan python tool.py master get user python tool.py master update user Singh 🔗 GitHub: https://lnkd.in/gXduQnez This project may look simple, but it gave me a deeper appreciation of how real databases manage data safely and efficiently. 💡 Next step: applying these concepts to more complex systems and scaling ideas further. Would love feedback or suggestions! #Python #BackendDevelopment #SystemDesign #Databases #CLI #LearningByBuilding

The biggest friction in learning SQL and Python isn't the concepts — it's the setup. Installing databases, configuring environments, debugging Docker containers, troubleshooting driver conflicts. Most beginners spend more time setting up than actually practicing. That's the problem In-Browser Practice on Let's Data Science eliminates entirely. 1,584 SQL and Python coding challenges run directly in your browser. Write your query or script, hit run, and get graded in milliseconds. No local installation, no environment configuration, no "it works on my machine" issues. What makes this different from a generic code playground: → 15 real industry datasets modeled after companies like Amazon, Google, Meta, Netflix, and LinkedIn — not contrived textbook examples → 4 difficulty levels from Easy to Expert, so you can progress at your own pace → Problems tagged by company name, letting you practice the exact style of questions asked at specific employers → Instant automated grading that checks your output against expected results — not just "does it run," but "is it correct" Whether you're preparing for a technical interview next week or building SQL fluency from scratch, the ability to open a browser tab and immediately start solving real-world problems removes every excuse between you and practice. Try any problem — many are free to attempt: https://lnkd.in/gYW7SyFH #DataScience #SQL #Python #LetsDataScience

🚀 select_related() vs prefetch_related() One of the most common performance issues in Django applications is the N+1 query problem. If you are not careful while fetching related data, your API or page can make dozens (or hundreds) of unnecessary database queries. Two powerful Django ORM tools help solve this: 🔹 select_related() - Uses SQL JOIN - Fetches related objects in a single query - Best for ForeignKey and OneToOne relationships 🔹 prefetch_related() - Executes separate queries - Joins the data in Python - Best for ManyToMany and reverse ForeignKey relationships 💡 Rule of thumb: - Use select_related() for single-valued relationships - Use prefetch_related() for multi-valued relationships Optimizing queries like this can dramatically reduce database load and speed up your Django APIs. I created a simple infographic to visually explain the difference 👇 What other Django performance techniques do you use in production? #django #python #backenddevelopment #webdevelopment #softwareengineering #databaseoptimization #orm #programming #djangoorm #codingtips

𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗣𝘆𝗧𝗵𝗼𝗻 𝗦𝘁𝗱𝗹𝗶𝗯 We built a multi-agent server with Python's standard library. Our server handles 200+ API endpoints, WebSocket connections, and 16 background daemons. You need a few key things when building a multi-agent coordination server: - HTTP API for external clients - WebSocket server for real-time agent push - Background threads for health monitoring and auto-restart - Atomic file operations for state persistence - No external dependencies that could break in production We chose Python's standard library over FastAPI. Here's why: - FastAPI would give us automatic OpenAPI docs and async request handling - But it would also give us extra dependencies and complexity We used: - http.server.HTTPServer for a simple HTTP handler - websockets library for the WebSocket server - threading for background tasks - tempfile + os.replace for atomic writes - json for state persistence We gave up automatic API documentation and request validation. But we gained: - Zero framework dependency risk - Full control over the execution model - Simple deployment - Predictable threading behavior Use the standard library when: - You need full control over threading and execution - Your server has mixed concerns - Deployment simplicity matters - You want zero framework lock-in Use FastAPI when: - You need automatic API docs - Your team expects framework conventions - You are building a pure REST API Source: https://lnkd.in/gCM8jKUD Optional learning community: https://lnkd.in/gAgpURzm

🆕 My latest blog is out which covers AWS Lambda Powertools for Python. It's the library I add to every Lambda function before writing any business logic, and I wanted to put everything I've learned from using it across real projects into one comprehensive guide. The post covers the core observability stack: Logger, Tracer, and Metrics - along with utilities like idempotency, batch processing, event routing, and parameters. I included Terraform and SAM examples from two of my own projects to keep things practical. If you're building with Lambda and spending more time on observability boilerplate than business logic, this might save you some effort. Three decorators can replace a surprising amount of code. Check it out! https://lnkd.in/evB8U4np

Actionpackd Knowledge bites - Day 46 What is flask in python ? Flask is a lightweight Python web framework used to build web applications and APIs quickly. It follows a minimalistic approach, giving developers full control instead of enforcing strict project structures. Key features : 1. Lightweight and flexible (micro-framework) 2. Built-in development server and debugger 3. Uses Jinja2 templating engine 4. REST API friendly 5. Easy integration with databases and extensions How it works ? 1. Define routes (URLs) using decorators 2. Each route maps to a Python function 3. Function processes request and returns response 4. Server renders output (HTML/JSON) Example use case • Backend for AI apps (e.g., serving a model via API) • Lightweight dashboards • MVPs and quick prototypes Why it’s popular ? • Simple to learn and start • Highly customizable • Large ecosystem of extensions , like Flask SQLAlchemy , Flask Login and more . #Actionpackd #KnowledgeBites #Flask #Python #AI